A triangulation, i.e. ranging by accurate determining of an angle within a triangle formed in space by two points of the optical system and a respective point of an object, is a standard method used in the 3D reconstruction of a space situated in front of an optical system. The triangulation can be carried out passively or actively. Conventionally, the passive triangulation is known, in which respectively one observer, for example a camera, is situated at the two points of the optical system. By way of example, this principle is realized in a stereo camera which records an object in space from different angles. However, the active triangulation, in which one observation point is replaced by a light source, i.e. the triangle consists of a light beam, irradiated object and camera, is the measurement principle most commonly used in optical ranging. Here, the ranging may also take place along a visual plane if a light stripe is projected in place of a light beam. A profile of the light stripe imaged in the camera can be converted into a distance profile along the light stripe by means of triangulation. Then, the fast measurement of a complete space is obtained by a structured light projection, in which different stripe patterns, for example with a stripe thickness that doubles in each recorded image of the sequence, are projected onto the object in quick temporal succession. Thus, the 3D reconstruction of the space can be calculated in real time from the known set of the stripe patterns and the images thereof in the camera. Here, the quality of the 3D reconstruction depends strongly on the correct identification of illuminated and non-illuminated areas.
3D reconstruction occurs in various technical applications, for example in a contactless measurement of objects, in medicine and dentistry, but especially in industry when controlling the form of workpieces or when designing a new form for a product. In real time, it is of essential importance to autonomously moving systems, the surrounding space of which needs to be explored. It is likewise used in a driver assistance system for assisting a driver of a motor vehicle and it forms the basis for a computer-controlled image analysis.
In a motor vehicle, the light source advantageously already is provided by a headlamp which, together with a camera installed in the front region, forms the optical system. Here, accurate knowledge about the vehicle-specific light distribution of the headlamp is of great importance for the quality of the image processing, making a calibration of a headlamp-camera system indispensable. By way of example, the document DE 10 2011 109 440 A1 describes a method which can be used to adjust and/or calibrate a headlamp of a vehicle.
By means of the headlamp, it is possible to project a pattern onto a vehicle near field, said pattern being used for image processing within an image recorded by the camera. Depending on a surface structure in the vehicle near field or on objects situated therein, the projected pattern is recorded in deformed fashion by the camera. A certain region which is of particular interest for a further evaluation may be stipulated for the image recorded by the camera. Such a region is commonly referred to as “region of interest”, also abbreviated as ROI in technical jargon.
According to the prior art, a feature search is carried out within the statically predetermined ROI during the image processing and at least one feature is extracted. Here, a feature consists of an imaging of a pattern projected by the headlamp which should represent a particularly good characteristic in the image. By way of example, such a pattern may consist of a checkerboard pattern. It should still be uniquely identifiable, even in the case of a strong deformation. The term “feature” is also used for this in technical jargon.
By way of example, the feature search may contain an edge detection. Various algorithms are known to this end. The Canny algorithm is specified here as an example; it supplies an image only still containing edges in the ideal case after carrying out various convolution operations.
The position of the found feature in the image of the camera and a geometric data record from the projected form of the headlamp form a so-called feature pair. The 3D reconstruction of the space situated in front of the headlamp-camera system is effectuated on the basis of the found feature pairs by means of triangulation; said 3D reconstruction may also consist of a pure depth map.
Identifying an erroneous feature occurs in the aforementioned feature search, the origin of said feature not lying in the projection of a pattern by the headlamp but said erroneous feature nevertheless being assigned a geometric data record from the headlamp and forming an erroneous feature pair therewith. What may occur particularly in the case of vehicle operation is that image components which do not contain a projected pattern repeatedly occur in the statically predetermined ROI, with the feature search however leading to erroneous feature pairs in said image component. A triangulation based on such an erroneous feature pair thus leads to an erroneous depth map.
In order to solve a problem of the static ROI—that it may contain image components without projected patterns—the prior art has proposed an adaptive restriction of the ROI to the projected pattern over the course of time. To this end, a light/dark boundary, abbreviated HDG in the German technical jargon, is extracted for each image recorded by the camera between the comparatively light projected pattern and the comparatively dark vehicle near field, and the ROI is adaptively segmented in this respect. The further image processing then only occurs in the continuously adapted ROI. However, a disadvantage of this procedure is that objects may appear in the regions in the vehicle near field predetermined by the light/dark boundary, said objects having a certain brightness but not originating from the projected pattern and then having erroneous feature pairs as a consequence.